Learn how AI agents leverage Monte Carlo Tree Search (MCTS) algorithms to calculate and select optimal future outcomes in this 26-minute educational video. Dive into the technical differences between Monte Carlo Particle Filters for dynamic system estimation and MCTS for decision-making simulation. Explore practical applications in healthcare, where MCTS helps create personalized treatment plans for chronic conditions by analyzing patient data, medical history, and real-time health monitoring information. Follow along with hands-on demonstrations using Python code and a free Google Colab notebook to understand the implementation of MCTS in AI systems. Master the strategy update process through self-play, understand the step-by-step MCTS explanation, and discover how to incorporate ethics filters into simulations. Examine real-world applications in medical and financial forecasting, while gaining insights into the "Tomorrow Machine" concept for optimizing future outcomes. Access provided resources include the original research paper on STRATEGIST, complete Python code on GitHub, and an interactive Colab notebook for practical experimentation. Read more